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In this paper we present a theoretical framework to understand the underlying psychological mechanism involved in human-Companion Robot interactions. At first, we take the case of Sexual Robotics, where the psychological dynamics are more evident, to thereafter extend the discussion to Companion Robotics in general. First, we discuss the differences between a sex-toy and a Sexual Robots, concluding that the latter may establish a collusive and confirmative dynamics with the user. We claim that the collusiveness leads to two main consequences, such as the fixation on a specific and atypical type of sexual interaction, called paraphilic, and to the infantilization of the user, which we explain through the theoretical framework of "object-relation theory". We argue that these dynamics may degrade to an infantile stage the relational abilities of users, extending this argument to Companion Robots in general. Then, we enquire if and how the relational dynamics enacted in HRI may shift to human relations: we discuss the analogy with virtual reality concluding that, under certain condition, a symbolic shift might happen. In the last part of this work, we propose an experimental setup to verify if a collusive and confirmative interaction with a Companion Robot can, over time, impact on the user's ability to manage relational frustration. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.robot.2008.08.008 Byline: Daniele Calisi, Luca Iocchi, Daniele Nardi, Carlo Matteo Scalzo, Vittorio Amos Ziparo Abstract: The need for improving the robustness, as well as the ability to adapt to different operational conditions, is a key requirement for a wider deployment of robots in many application domains. In this paper, we present an approach to the design of robotic systems, that is based on the explicit representation of knowledge about context. The goal of the approach is to improve the system's performance, by dynamically tailoring the functionalities of the robot to the specific features of the situation at hand. While the idea of using contextual knowledge is not new, the proposed approach generalizes previous work, and its advantages are discussed through a case study including several experiments. In particular, we identify many attempts to use contextual knowledge in several basic functionalities of a mobile robot such as: behavior, navigation, exploration, localization, mapping and perception. We then show how re-designing our mobile platform with a common representation of contextual knowledge, leads to interesting improvements in many of the above mentioned components, thus achieving greater flexibility and robustness in the face of different situations. Moreover, a clear separation of contextual knowledge leads to a design methodology, which supports the design of small specialized system components instead of complex self-contained subsystems. Author Affiliation: Dipartimento di Informatica e Sistemistica, Sapienza University of Rome, Via Ariosto 25, I-00185 Rome, Italy

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.robot.2006.06.007 Byline: Giorgio Grisetti (a)(b), Gian Diego Tipaldi (b), Cyrill Stachniss (c)(a), Wolfram Burgard (a), Daniele Nardi (b) Abstract: Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao-Blackwellized particle filters. Author Affiliation: (a) University of Freiburg, Department of Computer Science, D-79110 Freiburg, Germany (b) Dipartimento Informatica e Sistemistica, Universita 'La Sapienza', I-00198 Rome, Italy (c) Eidgenossische Technische Hochschule Zurich (ETH), IRIS, 8092 Zurich, Switzerland Article History: Received 1 October 2005; Revised 1 April 2006; Accepted 1 June 2006

Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures. [ABSTRACT FROM AUTHOR]

In order to operate in human-populated environments, robots need to show reasonable behaviors and human-compatible abilities. In the so-called Symbiotic Autonomy, robots and humans help each other to overcome mutual limitations and complete their tasks. When the robot takes the initiative and asks the human for help, there is a change of perspective in the interaction, which has not yet been specifically addressed by HRI studies. In this paper, we investigate the novel scenario brought about by Symbiotic Autonomy, by addressing the factors that may influence the interaction. In particular, we introduce the "Collaboration Attitude" to evaluate how the response of users being asked by the robot for help is influenced by the context of the interaction and by what they are doing (i.e., ongoing activity). We present the results of a first study, which confirms the influence of conventional factors (i.e., proxemics) on the Collaboration Attitude, while it suggests that the context (i.e., relaxing vs. working) may not be much relevant. Then, we present a second study, carried out to better assess the influence of the activity performed by the humans in our population, when (s)he is approached by the robot, as an additional and more compelling characterization of context (i.e., standing vs. sitting). While the experimental scenario takes into account a population with distinctive characteristics (i.e., academic staff and students), the overall findings of our studies suggest that the attitude of users towards robots in the setting of Symbiotic Autonomy is influenced by factors already known to influence robot acceptance while it is not significantly affected by the context of the interaction and by the human ongoing activity. [ABSTRACT FROM AUTHOR]

RoboCup has been a vehicle for promoting robotics and artificial intelligence (AI) research by offering a publicly appealing but formidable challenge. One effective way to promote science and engineering research is to set a challenging long-term goal. RoboCup was founded with such a goal: "By 2050, have a team of soccer robots defeat the team of World Cup champions." Our challenging goal has been expanded to include teams of rescue robots, those that service humans at home or in manufacturing tasks, and robotics soccer, rescue, and dancing motion challenges for educating children. [ABSTRACT FROM AUTHOR]

Multi-robot patrolling is a key feature for various applications related to surveillance and security, and it has been studied from several different perspectives, ranging from techniques that devise optimal off-line strategies to implemented systems. However, still few approaches consider on-line decision techniques that can cope with uncertainty and non-determinism in robot behaviors. In this article we address on-line coordination, by casting the multi-robot patrolling problem as a task assignment problem and proposing two solution techniques: DTA-Greedy, which is a baseline greedy approach, and DTAP, which is based on sequential single-item auctions. We evaluate the performance of our system in a realistic simulation environment (built with ROS and stage) as well as on real robotic platforms. In particular, in the simulated environment we compare our task assignment approaches with previous off-line and on-line methods. Our results confirm that on-line coordination approaches improve the performance of the multi-robot patrolling system in real environments, and that coordination approaches that employ more informed coordination protocols (e.g., DTAP) achieve better performances with respect to state-of-the-art online approaches (e.g., SEBS) in scenarios where interferences among robots are likely to occur. Moreover, the deployment on real platforms (three Turtlebots in an office environment) shows that our on-line approaches can successfully coordinate the robots achieving good patrolling behaviors when facing typical uncertainty and noise (e.g., localization and navigation errors) associated to real platforms. [ABSTRACT FROM AUTHOR]

The currently available speech technologies on mobile devices achieve effective performance in terms of both reliability and the language they are able to capture. The availability of performant speech recognition engines may also support the deployment of vocal interfaces in consumer robots. However, the design and implementation of such interfaces still requires significant work. The language processing chain and the domain knowledge must be built for the specific features of the robotic platform, the deployment environment and the tasks to be performed. Hence, such interfaces are currently built in a completely ad hoc way. In this paper, we present a design methodology together with a support tool aiming to streamline and improve the implementation of dedicated vocal interfaces for robots. This work was developed within an experimental project called Speaky for Robots. We extend the existing vocal interface development framework to target robotic applications. The proposed solution is built using a bottom-up approach by refining the language processing chain through the development of vocal interfaces for different robotic platforms and domains. The proposed approach is validated both in experiments involving several research prototypes and in tests involving end-users. [ABSTRACT FROM AUTHOR]

Scientific experiments and robotic competitions share some common traits that can put the debate about developing better experimental methodologies and replicability of results in robotics research on more solid ground. In this context, the Robot Competitions Kick Innovation in Cognitive Systems and Robotics (RoCKIn) project aims to develop competitions that come close to scientific experiments, providing an objective performance evaluation of robot systems under controlled and replicable conditions. In this article, by further articulating replicability into reproducibility and repeatability and by considering some results from the 2014 first RoCKIn competition, we show that the RoCKIn approach offers tools that enable the replicability of experimental results. [ABSTRACT FROM PUBLISHER]

The article describes the robot competitions, RoCKIn@Work, which looks for innovative robot applications in industry, and RoCKIn@Home, which focuses on domestic service robots, to be held in France in November 2014 and in Portugal in November 2015, as part of RoCKIn, a multiyear European Union-funded robotics project, which also consists of educational camps and field exercise. Topics include the benchmarks and tasks to be used in the contests and the RoCKIn Camp held in January 2014.

The creation of intelligent robots has been a major goal of Artificial Intelligence since the early days and has provided many motivations to Artificial Intelligence researchers. Therefore, a large body of research has been done in this field and many relevant results have shown that integration of Artificial Intelligence and Robotics techniques is a viable approach towards this goal. This article summarizes the efforts and the achievements of several Italian research groups in the development of intelligent robotic systems characterized by a suitable integration of Artificial Intelligence and Robotic techniques. The contributions collected in this article show the long history of this research stream, the impact of the developed approaches in the scientific community, and the efforts towards actual deployment of the developed systems. [ABSTRACT FROM AUTHOR]

“Exploration and search” is a typical task for autonomous robots performing in rescue missions, specifically addressing the problem of exploring the environment and at the same time searching for interesting features within the environment. In this paper, we model this problem as a multi-objective exploration and search problem and present a prototype system, featuring a strategic level, which can be used to adapt the task of exploration and search to specific rescue missions. Specifically, we make use of high-level representation of the robot plans through a Petri Net formalism that allows representing in a coherent framework decisions, loops, interrupts due to unexpected events or action failures, concurrent actions, and action synchronization. While autonomous exploration has been investigated in the past, we specifically focus on the problem of searching interesting features in the environment during the map building process. We discuss performance evaluation of exploration and search strategies for rescue robots, by using an effective performance metric, and present evaluation of our system through a set of experiments. © 2007 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Dias and colleagues about a survey and analysis of multirobot systems and another by Fox and colleagues about multirobot exploration and mapping.

Efficient robotic behaviors require robustness and adaptation to dynamic changes of the environment, whose characteristics rapidly vary during robot operation. To generate effective robot action policies, planning and learning techniques have shown the most promising results. However, if considered individually, they present different limitations. Planning techniques lack generalization among similar states and require experts to define behavioral routines at different levels of abstraction. Conversely, learning methods usually require a considerable number of training samples and iterations of the algorithm. To overcome these issues, and to efficiently generate robot behaviors, we introduce LoOP , an iterative learning algorithm for optimistic planning that combines state-of-the-art planning and learning techniques to generate action policies. The main contribution of LoOP is the combination of Monte-Carlo Search Planning and Q-learning, which enables focused exploration during policy refinement in different robotic applications. We demonstrate the robustness and flexibility of LoOP in various domains and multiple robotic platforms, by validating the proposed approach with an extensive experimental evaluation. • Novel approach to take advantage of both Robot learning and planning techniques. • Improve sample efficiency for tackling robotic tasks with large state-spaces. • Action policy generalization via Monte Carlo tree search and function approximation. • Extensive experimental evaluation of the proposed method in robotic scenarios. [ABSTRACT FROM AUTHOR]